The composition of the output layer architecture in a back-propagation (BP) neural network for remote sensing image classification

Guobin Zhu, Dan G. Blumberg

Research output: Contribution to journalConference articlepeer-review

Abstract

A Neural Network is treated as a data transformer when used for mapping purposes. The objective in this case, is to associate the elements in one set of data with the elements in a second set. According to this principle, three encoding methods, namely, single output layer, binary encoding, and ortho-encoding, have been designed for the output layer of a Neural Network based on five criteria, and put into experiments for Remote Sensing classification by means of a series of images coordinated with incremental noise level, from 1% to 10%. At last, the experiment results are assessed from different perspectives such as, accuracy, convergence, mixture detection, and confidence of classification, comparing to three encoding methods respectively.

Original languageEnglish
Pages (from-to)254-260
Number of pages7
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume33
StatePublished - 1 Jan 2000
Event19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 - Amsterdam, Netherlands
Duration: 16 Jul 200023 Jul 2000

Keywords

  • Classification
  • Neural network
  • Output layer architecture
  • Remote sensing

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

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